





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class   _SCREAMING_SNAKE_CASE		(      unittest.TestCase       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->str:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         ="ylacombe/bark-small"
   lowerCamelCase__: Tuple         =tempfile.mkdtemp()
   lowerCamelCase__: Tuple         ="en_speaker_1"
   lowerCamelCase__: Optional[int]         ="This is a test string"
   lowerCamelCase__: List[str]         ="speaker_embeddings_path.json"
   lowerCamelCase__: int         ="speaker_embeddings"

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					**UpperCAmelCase_							:  Any)			->Tuple:



   '''simple docstring'''



   return AutoTokenizer.from_pretrained(self.checkpoint			,					**UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Union[str, Any]:



   '''simple docstring'''



   shutil.rmtree(self.tmpdirname)

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Any:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.get_tokenizer()

   lowerCamelCase__: List[str]         =BarkProcessor(tokenizer=UpperCAmelCase_)

   processor.save_pretrained(self.tmpdirname)
   lowerCamelCase__: Dict         =BarkProcessor.from_pretrained(self.tmpdirname)

   self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer.get_vocab())

 @slow
 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =BarkProcessor.from_pretrained(
       pretrained_processor_name_or_path=self.checkpoint			,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,					)
   processor.save_pretrained(
       self.tmpdirname			,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,					speaker_embeddings_directory=self.speaker_embeddings_directory			,					)

   lowerCamelCase__: Dict         =self.get_tokenizer(bos_token="(BOS)"			,					eos_token="(EOS)")

   lowerCamelCase__: Any         =BarkProcessor.from_pretrained(
       self.tmpdirname			,					self.speaker_embeddings_dict_path			,					bos_token="(BOS)"			,					eos_token="(EOS)"			,					)

   self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab())

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->int:



   '''simple docstring'''



   lowerCamelCase__: Any         =BarkProcessor.from_pretrained(
       pretrained_processor_name_or_path=self.checkpoint			,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,					)

   lowerCamelCase__: List[str]         =35
   lowerCamelCase__: Optional[Any]         =2
   lowerCamelCase__: Optional[Any]         =8

   lowerCamelCase__: Optional[int]         ={
       "semantic_prompt": np.ones(UpperCAmelCase_),
       "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
       "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
   }

   # test providing already loaded voice_preset
   lowerCamelCase__: Any         =processor(text=self.input_string			,					voice_preset=UpperCAmelCase_)

   lowerCamelCase__: int         =inputs["history_prompt"]
   for key in voice_preset:
     self.assertListEqual(voice_preset[key].tolist()			,					processed_voice_preset.get(UpperCAmelCase_			,					np.array([])).tolist())

   # test loading voice preset from npz file
   lowerCamelCase__: Union[str, Any]         =os.path.join(self.tmpdirname			,					"file.npz")
   np.savez(UpperCAmelCase_			,					**UpperCAmelCase_)
   lowerCamelCase__: Tuple         =processor(text=self.input_string			,					voice_preset=UpperCAmelCase_)
   lowerCamelCase__: Optional[Any]         =inputs["history_prompt"]

   for key in voice_preset:
     self.assertListEqual(voice_preset[key].tolist()			,					processed_voice_preset.get(UpperCAmelCase_			,					np.array([])).tolist())

   # test loading voice preset from the hub
   lowerCamelCase__: Any         =processor(text=self.input_string			,					voice_preset=self.voice_preset)







 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__: str         =self.get_tokenizer()

   lowerCamelCase__: Dict         =BarkProcessor(tokenizer=UpperCAmelCase_)

   lowerCamelCase__: List[Any]         =processor(text=self.input_string)

   lowerCamelCase__: Optional[int]         =tokenizer(
       self.input_string			,					padding="max_length"			,					max_length=256			,					add_special_tokens=UpperCAmelCase_			,					return_attention_mask=UpperCAmelCase_			,					return_token_type_ids=UpperCAmelCase_			,					)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]			,					encoded_processor[key].squeeze().tolist())





'''simple docstring'''


import numpy as np





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= (0, 0)
											_SCREAMING_SNAKE_CASE					:			List[str]							= None
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0
											_SCREAMING_SNAKE_CASE					:			List[str]							= 0
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0






							def __eq__(       self       ,   __snake_case    ):
											return self.position == cell.position






							def UpperCAmelCase_      (       self    ):
											print(self.position    )





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case=(5, 5)    ):
											_SCREAMING_SNAKE_CASE					:			str							= np.zeros(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= world_size[0]
											_SCREAMING_SNAKE_CASE					:			Tuple							= world_size[1]






							def UpperCAmelCase_      (       self    ):
											print(self.w    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= [
											    (-1, -1),
											    (-1, 0),
											    (-1, 1),
											    (0, -1),
											    (0, 1),
											    (1, -1),
											    (1, 0),
											    (1, 1),
											]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[0]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[1]
											_SCREAMING_SNAKE_CASE					:			Tuple							= []
											for n in neughbour_cord:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= current_x + n[0]
															_SCREAMING_SNAKE_CASE					:			List[Any]							= current_y + n[1]
															if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= Cell()
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (x, y)
																			_SCREAMING_SNAKE_CASE					:			Dict							= cell
																			neighbours.append(__snake_case    )
											return neighbours







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= []
				_open.append(SCREAMING_SNAKE_CASE__	)

				while _open:
								_SCREAMING_SNAKE_CASE					:			Any							= np.argmin([n.f for n in _open]	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= _open[min_f]
								_closed.append(_open.pop(SCREAMING_SNAKE_CASE__	)	)
								if current == goal:
												break
								for n in world.get_neigbours(SCREAMING_SNAKE_CASE__	):
												for c in _closed:
																if c == n:
																				continue
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= current.g + 1
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= n.position
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= goal.position
												_SCREAMING_SNAKE_CASE					:			Dict							= (ya - ya) ** 2 + (xa - xa) ** 2
												_SCREAMING_SNAKE_CASE					:			Any							= n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= []
				while current.parent is not None:
								path.append(current.position	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= current.parent
				path.append(current.position	)
				return path[::-1]


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						Gridworld()
				# Start position and goal
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(0, 0)
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(4, 4)
				print(F"path from {start.position} to {goal.position}")
				UpperCAmelCase_						:  Union[str, Any]				  =						astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								UpperCAmelCase_						:  Optional[int]				  =						1
				print(world.w)




